Module 1: Highlights

Language Probability and Representation

Generative AI
Language Modeling
Probability
NLP Foundations
Published

January 1, 2026

Modified

February 17, 2026

1 Lecture 1.1: Course Orientation and Reproducible GenAI Setup

1.1 Highlights

  • Framing Generative AI as a probabilistic system, not a deterministic reasoning engine.
  • Course structure, deliverables, and reproducible experimentation standards.
  • Establishing a structured GenAI workflow (environment, versioning, logging).
  • Introduction to AI disclosure files, documentation practices, and accountability.
  • Understanding GenAI as a socio-technical system requiring governance.

1.2 Learning Objectives

By the end of this lecture, students will be able to:

  • Explain why GenAI systems operate probabilistically.
  • Set up a reproducible environment for GenAI experimentation.
  • Describe the importance of AI disclosure and documentation.
  • Frame generative models as system components within larger workflows.

2 Lecture 1.2: Language Probability and Generative Systems

2.1 Highlights

  • Language as a probability distribution over token sequences.
  • Prediction as the fundamental mechanism behind generation.
  • Conditional likelihood and uncertainty in text modeling.
  • The relationship between NLP foundations and modern large language models.
  • Introduction to entropy and uncertainty as behavioral drivers in generative systems.

2.2 Learning Objectives

By the end of this lecture, students will be able to:

  • Interpret generation as next-token probability prediction.
  • Explain conditional probability in language modeling.
  • Connect uncertainty to hallucination and variability in outputs.
  • Understand why NLP theory underpins modern GenAI systems.